Burned Area Emergency Rehabilitation Research Articles

Methods to evaluate floods on a burned watershed

This updated study focused on the methodologies, tools, and applications used to evaluate the hydrologic runoff potential of a burned watershed when few variables were actually measured. Forest fi res often alter the balance between rainfall and the resulting runoff from natural watersheds. This may result in fl ooding from the burned watershed at locations downstream. Such was the case for the Mud Canyon watershed on New Mexico’s Mescalero Apache Indian Reservation. In the spring, a fi re burned approximately 42% of the watershed. Subsequent storms during the summer caused fl ows in Mud Canyon that led to fldownstream at the town of Mescalero. The fl ooding was likely the result of inadequate remediation, neither human nor natural, during the months between the fi re and the storms. While the summer storms that followed the spring fi re had an updated magnitude to be expected every 10 years, the resulting updated fl ooding was more on the order of a 500-year event. A purpose of this study was to determine the amount of rainfall that produced the estimated fl ood fl ows. This result could then be used to determine the degree to which the burned portion of the watershed caused the fl ooding. The Burned Area Emergency Rehabilitation (BAER) Plan concluded that there was not a serious threat of flbased on the fact that the soils in the watershed were well-drained and not hydrophobic. The paper concludes that the loss of ground cover, particularly for relatively steep watersheds, should be seriously considered when evaluating the potential for fl ooding on a burned watershed. The methods used for this updated hydrologic analysis of Mud Canyon, as outlined in this paper, are applicable for future analyses of burned watersheds to determine the extent to which loss of ground cover contributes to increased fl ood fl ows.

Evaluating Spectral Indices for Assessing Fire Severity in Chaparral Ecosystems (Southern California) Using MODIS/ASTER (MASTER) Airborne Simulator Data

Wildland fires are a yearly recurring phenomenon in many terrestrial ecosystems. Accurate fire severity estimates are of paramount importance for modeling fire-induced trace gas emissions and rehabilitating post-fire landscapes. We used high spatial and high spectral resolution MODIS/ASTER (MASTER) airborne simulator data acquired over four 2007 southern California burns to evaluate the effectiveness of 19 different spectral indices, including the widely used Normalized Burn Ratio (NBR), for assessing fire severity in southern California chaparral. Ordinal logistic regression was used to assess the goodness-of-fit between the spectral index values and ordinal field data of severity. The NBR and three indices in which the NBR is enhanced with surface temperature or emissivity data revealed the best performance. Our findings support the operational use of the NBR in chaparral ecosystems by Burned Area Emergency Rehabilitation (BAER) projects, and demonstrate the potential of combining optical and thermal data for assessing fire severity. Additional testing in more burns, other ecoregions and different vegetation types is required to fully understand how (thermally enhanced) spectral indices relate to fire severity.

Characterization of post-fire surface cover, soils, and burn severity at the Cerro Grande Fire, New Mexico, using hyperspectral and multispectral remote sensing

Forest fires leave behind a changed ecosystem with a patchwork of surface cover that includes ash, charred organic matter, soils and soil minerals, and dead, damaged, and living vegetation. The distributions of these materials affect post-fire processes of erosion, nutrient cycling, and vegetation regrowth. We analyzed high spatial resolution (2.4 m pixel size) Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data collected over the Cerro Grande fire, to map post-fire surface cover into 10 classes, including ash, soil minerals, scorched conifer trees, and green vegetation. The Cerro Grande fire occurred near Los Alamos, New Mexico, in May 2000. The AVIRIS data were collected September 3, 2000. The surface cover map revealed complex patterns of ash, iron oxide minerals, and clay minerals in areas of complete combustion. Scorched conifer trees, which retained dry needles heated by the fire but not fully combusted by the flames, were found to cover much of the post-fire landscape. These scorched trees were found in narrow zones at the edges of completely burned areas. A surface cover map was also made using Landsat Enhanced Thematic Mapper plus (ETM+) data, collected September 5, 2000, and a maximum likelihood, supervised classification. When compared to AVIRIS, the Landsat classification grossly overestimated cover by dry conifer and ash classes and severely underestimated soil and green vegetation cover. In a comparison of AVIRIS surface cover to the Burned Area Emergency Rehabilitation (BAER) map of burn severity, the BAER high burn severity areas did not capture the variable patterns of post-fire surface cover by ash, soil, and scorched conifer trees seen in the AVIRIS map. The BAER map, derived from air photos, also did not capture the distribution of scorched trees that were observed in the AVIRIS map. Similarly, the moderate severity class of Landsat-derived burn severity maps generated from the differenced Normalized Burn Ratio (dNBR) calculation had low agreement with the AVIRIS classes of scorched conifer trees. Burn severity and surface cover images were found to contain complementary information, with the dNBR map presenting an image of degree of change caused by fire and the AVIRIS-derived map showing specific surface cover resulting from fire.

Classifying and Mapping Wildfire Severity

This study evaluates six different approaches to classifying and mapping fire severity using multi-temporal Landsat Thematic Mapper data. The six approaches tested include: two based on temporal image differencing and ratioing between pre-fire and post-fire images, two based on principal component analysis of pre- and post-fire imagery, and two based on artificial neural networks, one using just postfire imagery and the other both pre- and post-fire imagery. Our results demonstrated the potential value for any of these methods to provide quantitative fire severity maps, but one of the image differencing methods (ND4/7) provided a flexible, robust, and analytically simple approach that could be applied anywhere in the Continental U.S. Based on the results of this test, the ND4/7 was implemented operationally to classify and map fire severity over 1.2 million hectares burned in the Northern Rocky Mountains and Northern Great Plains during the 2000 fire season, as well as the 2001 fire season (Gmelin and Brewer, 2002). Approximately the same procedure was adopted in 2001 by the USDA Forest Service, Remote Sensing Applications Center to produce Burned Area Reflectance Classifications for national-level support of Burned Area Emergency Rehabilitation activities (Orlemann, 2002).

Ecological impacts of wheat seeding after a Sierra Nevada wildfire

The Highway Fire burned 1680 ha of mixed ponderosa pine–oak–chaparral in the newly created Giant Sequoia National Monument and the adjacent Sequoia National Forest of Fresno County, California in August 2001. The USDA Forest Service Burned Area Emergency Rehabilitation (BAER) program recommended that portions of the burned forest be seeded with a non-persistent variety of wheat at a density of 157 kg ha–1 (140 lb/ac). The present study compared the vascular plant diversity and cover in seeded and unseeded parts of this burn to evaluate the ecological impact of seeding an alien grass. In the first post-fire growing season, the natural regeneration of unseeded control sites averaged ~55% ground surface covered. Wheat seeding enhanced the ground cover, averaging 95% ground surface cover. Wheat was the dominant species on the seeded sites, comprising 67% of the total cover. Dominance–diversity curves were markedly affected by the seeding and indicated a disruption in the natural ecological structure of these communities. On seeded sites, wheat dominated and all other species were poorly represented whereas, on unseeded control sites, there was a more equitable distribution of species. Correlated with the wheat cover was a significant decrease in species richness at all scales examined. Total species richness was reduced from 152 species across all unseeded sites to 104 species on all seeded sites. Average species richness, at scales from 1 to 1000 m2, was 30–40% lower on seeded sites. Species most strongly inhibited were post-fire endemics whose lifecycle is restricted to immediate post-fire environments. Seeded sites had fewer alien species than unseeded sites; however, this may not have any lasting effect since other studies show the primary alien threat is not in the first post-fire year. Seeding was also associated with an order of magnitude drop in Pinus ponderosa seedling recruitment and, coupled with the massive thatch still remaining on the site, it is likely that recruitment will be inhibited in subsequent years.

Modeling potential erosion due to the Cerro Grande Fire with a GIS-based implementation of the Revised Universal Soil Loss Equation

Erosional processes directly influenced by wildland fire include reduction or elimination of above- ground biomass, reduction of soil organic matter, and hydrophobicity. High fuel loads promoted by decades of fire suppression in the U.S. increase the duration and intensity of burning, amplifying these effects. The Cerro Grande fire (6–31 May 2000) consumed approximately 15 000 hectares around and within the town of Los Alamos, New Mexico, USA. Private and public infrastructure including Los Alamos National Laboratory are at continuing risk due to increased threats of upstream erosion. We use a geographic information system (GIS) based implementation of the Revised Universal Soil Loss Equation (RUSLE) to model pre- and post-fire soil loss conditions and aid erosion risk analysis. Pre- and post-fire vegetation cover data layers were generated from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) data. Based upon annual average rainfall amounts we estimate that subwatershed average pre-fire erosion rates range from 0.45 to 9.22 tonnes ha–1 yr–1 while post-fire erosion rates before watershed treatments range from 1.72 to 113.26 tonnes ha–1 yr–1. Rates are approximately 3.7 times larger for 50 year return interval rainfall amounts. It is estimated that watershed treatments including reseeding will decrease soil loss between 0.34 and 25.98% in the first year on treated subwatersheds. Immediately after the fire an interagency Burned Area Emergency Rehabilitation (BAER) team produced initial estimates of soil erosion. Our estimates of average erosion rates by subwatershed were in general larger than those initial estimates.

Mapping forest post-fire canopy consumption in several overstory types using multi-temporal Landsat TM and ETM data

To facilitate the identification of appropriate post-fire watershed treatments and minimize erosion effects after socio-economically important fires, Interagency Burned Area Emergency Rehabilitation (BAER) teams produce initial timely estimates of the fire perimeter and classifications of burn severity, forest mortality, and vegetation mortality. Accurate, cost-effective, and minimal time-consuming methods of mapping fire are desirable to assist rehabilitation efforts immediately after containment of the fire. BAER teams often derive their products by manually interpreting color infrared aerial photos and/or field analysis. Automated classification of multispectral satellite data are examined to determine whether they can provide improved accuracy over manually digitized aerial photographs. In addition, pre-fire vegetation data are incorporated to determine whether further gains in accuracy of mapped canopy consumption can be made. BAER team classifications from the Cerro Grande Fire were compared to estimates of overstory consumption produced using a pre-fire vegetation classification, and a change detection algorithm using bands 4 and 7 from July 1997 pre-fire Landsat Thematic Mapper (TM) and July 2000 post-fire Enhanced Thematic Mapper (ETM) data. BAER team classifications are highly correlated to overstory consumption and should produce high Kappa statistics when verified using the same dataset. Our three-class supervised classification of the change image incorporating a pre-fire vegetation classification yielded the highest Kappa at 0.86. A three-class unsupervised classification of the change image yielded a lower Kappa of 0.72. BAER team classifications yielded Kappas ranging from 0.38 to 0.63 using the same verification dataset.